Biopic

Prof. Lyudmila Mihaylova and her team are working to develop novel methods for autonomous intelligent systems: for sensing, tracking and decision making, machine learning and their engineering applications. Prof. Mihaylova undertook pioneering work on traffic flow estimation with particle filtering for intelligent transportation systems which was followed later with developments for large scale systems, including large scale transportation and video processing systems. She has experience with a range of image modalities, including optical, thermal, LIDAR, SAR and hyperspectral image processing. Previously she had academic positions with Lancaster University (2006-2013), University of Bristol (2004-2006), and research visiting positions with the University of Ghent, Belgium, the Katholic University of Leuven, Belgium and the Bulgarian Academy of Sciences, Bulgaria. Her interests are in the area of nonlinear filtering, sequential Monte Carlo Methods, statistical signal processing and sensor data fusion. Her work involves the development of novel techniques, e.g. for high dimensional problems (including vehicular traffic flow estimation and image processing) and localisation and positioning in sensor networks. Prof. Mihaylova is an Associate Editor of the IEEE Transactions on Aerospace and Electronic Systems and an Associate Editor of Elsevier Signal Processing Journal. Prof. Mihaylova is a senior member of the IEEE, Signal Processing Society, the President of the International Society of Information Fusion (ISIF) and an ISIF board member.

Prof. Mihaylova has also been serving the scientific community as a member of the Programme/ Organising Committee of international conferences and symposia, including the International Conferences on Information Fusion, the American Control Conferences, EUSIPCO, conferences on Intelligent Transportation Systems and the German workshops on Multiple Sensor Data Fusion. She has given a number of invited talks, e.g. the keynote speech for the 5th IET International Conference on Wireless, Mobile and Multimedia Networks (2013), Beijing, China, and tutorials including for the EU Marie Curie ITN (2010, Sweden, 2012, Germany) and COST-NEARCTIS workshop (2010, Switzerland). Her research is funded by sponsors such as EPSRC, EU, MOD and industry.

Other graduated PhD students

I am grateful to the sponsors of my research: EPSRC, MoD/DSTL, EU, industrial and other partners. A brief description of projects of mine is given below.

Current Grants

NSF-EPSRC: ShiRAS. Towards Safe and Reliable Autonomy in Sensor Driven Systems, EP/T013265/1, PI, 2019-2022, £217,000. Modern data-driven algorithms trained over enormous datasets have revolutionised contemporary autonomous systems with their accurate predictive power. However, due to technical limitations, it is a challenge to integrate large-scale data from many different and complex sensors. Capturing the confidence of these algorithms also remains a challenge. In response to this demand, ShiRAS will develop pioneering approaches that will introduce autonomy at different levels in sensor-driven systems. The main focus is on machine learning methods with quantified uncertainty of the provided solutions. Within the field of machine learning, deep learning approaches have resulted in the state-of-the-art accuracy in visual object detection, speech recognition and translation, and many other domains. Deep learning can discover intricate structure in large data sets by using multiple levels of representation, where each level is a higher, more abstract representation of the data. However, a rigorous mathematical framework for uncertainty propagation and update in machine learning models has been largely underexplored. Most current deep learning techniques process the raw data in a deterministic way and do not capture model confidence or trust. Uncertainty can emanate from the noise in the raw data and the parameters of the approach and this impact is a critical part for any predictive system's output. By representing the unknown parameters using distributions instead of point estimates and propagating these distributions from the input to the output of the system, we propose promising machine learning methods able to handle uncertainty in a unified way.

Confident safety integration for Cobots (CSI: Cobot), 2019-2020, funded by Lloyd's Register Foundation, co-I, PI Dr James Law, jointly with other co-Is Prof. John Clark, Dr Jonathan Aitken, Dr Radu Calinescu (University of York) and Dr Rob Alexander (University of York). This project will demonstrate how novel safety techniques can be applied to build confidence in the deployment of uncaged collaborative robot systems operating in spaces shared with users. Existing collaborative processes provided by our industrial partners will act as case studies and demonstrators. These vary in complexity, but are suitably constrained in that they provide a tractable safety problem whilst providing a good representation of current industry applications and needs.

SETA created technology and methodology for changing the way mobility is organised, monitored and planned in large metropolitan areas. The solutions focused on the management of high-volume, high-velocity, multi-dimensional, heterogeneous, cross-media, cross-sectoral data and information which is sensed, crowdsourced, acquired, linked, fused, and used to model mobility with a precision, granularity and dynamicity that is impossible with today’s technologies. Such models provide always-on, pervasive services to citizens and business, as well as decision makers to support safe, sustainable, effective, efficient and resilient mobility. The consortium involves partners from 5 countries, UK, Italy, Spain, Poland and The Netherlands. Funded by EU, the project partners are University of Sheffield (Lead), Delft University (NL), University of Cantabria (Spain), Sheffield Hallam University (UK), Knowledge now Limited (UK), The FLOOW Limited (UK), TSS-TRANSPORT SIMULATION SYSTEMS SL (Spain), Software Mind SA (Poland), AIZOON Consulting SRL (Italy), AYUNTAMIENTO DE SANTANDER (Spain), Citta di Torino (Italy), Birmingham City Council, Scyfer B.V. (NL).

Mobility grant with China, funded by the Royal Academy of Engineering 1 April 2016 - 31 March 2018, £9,500.

The TRAX research project is an International Training Network (ITN) sponsored by the EU under the Marie Curie actions in the Seventh Framework Program (FP7). TRAX concentrates on Tracking in Complex Sensor Systems, focussing on complexities due to large volumes of data, complex object dynamics and measurement models, as well as large scale systems. The project partners are a well-known and well-respected mixture of academia, research institutes, small and large business companies, including University of Sheffield and Rinicom from the United Kingdom, Linköping University and Ericsson from Sweden, Fraunhofer FKIE from Germany, and University of Twente, Xsens and Thales from The Netherlands.

Remote Sensing for Hedgerow Assessment in Agricultural Areas, Grantham centre grant (March-July 2017), £9,567. There is an imminent necessity to help analyse data of many types in automated and semi-automated manners, especially multispectral, hyperspectral, thermal, ISAR and LiDAR data. This project aims to ensemble methods for imagery data to support analysis of hedgerows in agricultural areas by synthesising data from multiple sources.

BTaRoT: Bayesian Tracking and Reasoning over Time , £446,037, EP/K021516/1, EPSRC funded (2013-2016)
This EPSRC funded project provided new advances in methods for reasoning about many objects that evolve in a scene over time. Information about such objects arrives, typically in a real-time data feed, from sensors such as radar, sonar, LIDAR and video. The Bayesian methodology is adopted due to its power to solve a wealth of complex inference problems, to take into account prior information and to incorporate it in flexible manners in the solutions. The main focus is on scalable approaches able to deal both with groups composed of a small number of objects (up to 20) and large groups, consisting of hundreds Partners for the project: Prof. Simon Godsill, Dr Sumeetpal S. Singh from Cambridge University and supported by QinetiQ.

This project focuses on extracting knowledge from large amounts of data and fusion methods related with tracking and behavior analysis. The main approach is based on combinatorial pattern matching and statistical data mining methods. The project is part of the Centres of Excellence funded by Selex Gallileo and comprises Aberdeen University, Cambridge University, Cranfield University, University of Sheffield, Lancaster University, and Robert Gordon University.

PhD Projects

For a current list of PhD projects related to my areas of research please see the PhD Research Projects web pages. The Figures below show results from the methods and technique developed by me and my team.